Friday, June 9, 2017

Still a Few Bugs In the System: "DeepMind Shows AI Has Trouble Seeing Homer Simpson's Actions"

From IEEE Spectrum:

The best artificial intelligence still has trouble visually
recognizing many of Homer Simpson’s favorite behaviors such as drinking
beer, eating chips, eating doughnuts, yawning, and the occasional
face-plant. Those findings from DeepMind, the pioneering London-based AI
lab, also suggest the motive behind why DeepMind has created a huge new
dataset of YouTube clips to help train AI on identifying human actions
in videos that go well beyond “Mmm, doughnuts” or “Doh!”

The most popular AI used by Google, Facebook, Amazon, and other
companies beyond Silicon Valley is based on deep learning algorithms
that can learn to identify patterns in huge amounts of data. Over time,
such algorithms can become much better at a wide variety of tasks such
as translating between English and Chinese for Google Translate or automatically recognizing the faces
of friends in Facebook photos. But even the most finely tuned deep
learning relies on having lots of quality data to learn from. To
help improve AI’s capability to recognize human actions in
motion, DeepMind has unveiled its Kinetics dataset consisting of 300,000
video clips and 400 human action classes.

“AI systems are now very good at recognizing objects in images, but
still have trouble making sense of videos,” says a DeepMind
spokesperson. “One of the main reasons for this is that the research
community has so far lacked a large, high-quality video dataset.”

DeepMind enlisted the help of online workers through Amazon’s
Mechanical Turk service to help correctly identify and label the actions
in thousands of YouTube clips. Each of the 400 human action classes in
the Kinetics dataset has at least 400 video clips, with each clip
lasting around 10 seconds and taken from separate YouTube videos. More
details can be found in a DeepMind paper on the arXiv preprint server.

The new Kinetics dataset seems likely to represent a new benchmark
for training datasets intended to improve AI computer vision for video.
It has far more video clips and action classes than the HMDB-51 and
UCF-101 datasets that previously formed the benchmarks for the research
community. DeepMind also made a point of ensuring it had a diverse
dataset—one that did not include multiple clips from the same YouTube
videos....

Computer honchos work on a section of Harvard's Mark I in 1944. The whole apparatus measured 55 feet long.

Courtesy Computer History Museum
__1944: __Harvard and IBM dedicate the Mark I computer. Also known as
the IBM Automatic Sequence Controlled Calculator, or ASCC, the
pioneering computer was notable for producing reliable results and its
ability to run 24/7....

Which was itself stolen from a computer reference in Doonesbury, 1970: